Outlier-resistant guided wave dispersion curve recovery and measurement placement optimization base on multitask complex hierarchical sparse Bayesian learning

Due to extensive detection range and high sensitivity to defects, ultrasonic Lamb waves are extensively studied in the fields of Nondestructive Testing and Structural Health Monitoring. In scenarios where the material parameters or geometric parameters of the waveguide are unknown, the dispersion relation of the guided wave cannot be calculated by the forward model. Consequently, it becomes imperative to extract wave propagation characteristics of Lamb wave from the acquired Lamb wave data. This paper presents a multitask complex hierarchical sparse Bayesian learning (MuCHSBL) method which is aimed at enhancing the efficacy of the dispersion relation solution by considering the continuity of the recovered dispersion curve in the frequency-wavenumber domain. Furthermore, the posterior distributions quantified by MuCHSBL are employed to optimize the placement of measurement points. Numerical and experimental studies are conducted to verify the effectiveness of the proposed method. Comparison analysis with the conventional approach demonstrates the significant enhancement in accuracy of recovering dispersion curves by the proposed method.